Hybrid deep learning and metaheuristic model based stroke diagnosis system using electroencephalogram (EEG)

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dc.authoridRadwan Qasrawi / 0000-0001-8671-7026en_US
dc.authorscopusidRadwan Qasrawi / 57212263325
dc.authorwosidRadwan Qasrawi / AAA-6245-2019en_US
dc.contributor.authorSawan, Aktham
dc.contributor.authorAwad, Mohammed
dc.contributor.authorQasrawi, Radwan
dc.contributor.authorSowan, Mohammad
dc.date.accessioned2023-11-06T10:37:19Z
dc.date.available2023-11-06T10:37:19Z
dc.date.issued2023en_US
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.description.abstractOver the last few decades, there has been a significant increase in the average lifespan. Consequently, the number of elderly people suffering from strokes has also risen. As a result, strokes and their treatments have become crucial subjects of research, particularly for the application of machine learning. One of the primary factors in stroke treatment is the speed of response. Currently, both computed tomography (CT) and magnetic resonance imaging (MRI) are used to diagnose strokes. However, CT takes eight hours before an accurate diagnosis can be made, and MRI is expensive and not available in all hospitals. Therefore, there is a growing need for novel approaches to identifying strokes based on electroencephalogram (EEG) signals. In this paper, a hybrid model of deep learning and metaheuristic was developed in the offline stage to classify strokes. Since EEG data is a time series with frequencies, a hybrid model was deemed appropriate. This hybrid model combined a Convolutional Neural Network (CNN) with bidirectional Gated Recurrent Unit (BiGRU). The performance of this model surpassed that of other comparable models. Given the paramount importance of speed and accuracy in this work, the harmony search (HS) algorithm, which is specialized in handling frequencies, was used for feature selection. HS outperformed all similar algorithms when applied to the CNN-BiGRU hybrid model. Additionally, for the optimization of continuous hyperparameters, the multiverse optimization (MVO) algorithm was employed, which proved to be the most effective when compared to another similar algorithm for validation purposes. The new model, CNN-BiGRU-HS-MVO, was applied to analyze the data collected from Al Bashir Hospital using the MUSE-2 portable device, resulting in an impressive prediction accuracy of 99.991%. Moreover, it demonstrated an 11.08% improvement over the results from the paper titled “Predicting stroke severity with a 3-min recording from the Muse portable EEG study”. Furthermore, a decision support system was built on the cloud computing environment based on the hybrid model. This system allows for the diagnosis of patients anytime and from anywhere within minutes, with the authorized person receiving the diagnosis results through SMS notification.en_US
dc.identifier.citationSawan, A., Awad, M., Qasrawi, R., & Sowan, M. (2024). Hybrid deep learning and metaheuristic model based stroke diagnosis system using electroencephalogram (EEG). Biomedical Signal Processing and Control, 87, 105454.en_US
dc.identifier.doi10.1016/j.bspc.2023.105454en_US
dc.identifier.issn1746-8094en_US
dc.identifier.issue1en_US
dc.identifier.scopus2-s2.0-85171785274en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.bspc.2023.105454
dc.identifier.urihttps://hdl.handle.net/20.500.12713/3993
dc.identifier.volume87en_US
dc.identifier.wosWOS:001082256200001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorQasrawi, Radwan
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofBiomedical Signal Processing and Controlen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectStroke Diagnosisen_US
dc.subjectElectroencephalogram (EEG)en_US
dc.subjectDeep Learning (DL)en_US
dc.subjectMetaheuristicen_US
dc.subjectMuse-2 Wearable Devicesen_US
dc.titleHybrid deep learning and metaheuristic model based stroke diagnosis system using electroencephalogram (EEG)en_US
dc.typeArticleen_US

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